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Letter D
Data Augmentation

Data augmentation is a technique used to increase the diversity of data available for training machine learning models without actually collecting new data. It involves creating modified versions of existing data points, such as rotating or flipping images.

Use Cases

Image Classification

Enhancing the training dataset by creating variations of existing images.

Natural Language Processing

Generating paraphrases of text data to improve model robustness.

Speech Recognition

Adding noise to audio data to make models more resilient to variations.

Importance

Improves Generalization

Helps models perform better on unseen data by exposing them to a wider variety of training examples.

Reduces Overfitting

Prevents models from memorizing the training data by introducing variability.

Cost-Effective

Enhances datasets without the need for expensive data collection processes.

Analogies

Data augmentation is like practising different variations of a dance routine. By rehearsing with slight changes in speed, direction, or style, dancers become more adaptable and capable of performing well under various conditions

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